gemma2 impl

model/models/gemma2/model.go

@@ -0,0 +1,206 @@
+package gemma2
+
+import (
+	"math"
+
+	"github.com/ollama/ollama/kvcache"
+	"github.com/ollama/ollama/ml"
+	"github.com/ollama/ollama/ml/nn"
+	"github.com/ollama/ollama/model"
+	"github.com/ollama/ollama/model/input"
+)
+
+type Options struct {
+	hiddenSize, numHeads, numKVHeads int
+	attnKeyLen, attnValLen           int
+	eps, ropeBase, ropeScale         float32
+	attnLogitSoftcap                 float32
+	finalLogitSoftcap                float32
+	largeModelScaling                bool
+}
+
+type Model struct {
+	model.Base
+	model.SentencePieceModel
+
+	TokenEmbedding *nn.Embedding `gguf:"token_embd"`
+	Layers         []Layer       `gguf:"blk"`
+	OutputNorm     *nn.RMSNorm   `gguf:"output_norm"`
+	Output         *nn.Linear    `gguf:"output,alt:token_embd"` // just set to token_embd?
+
+	*Options
+}
+
+const (
+	gemma27BLayerCount = 46
+)
+
+func New(c ml.Config) (model.Model, error) {
+	m := Model{
+		SentencePieceModel: model.NewSentencePieceModel(
+			c.String("tokenizer.ggml.pretokenizer", `(?i:'s|'t|'re|'ve|'m|'ll|'d)|[^\r\n\p{L}\p{N}]?\p{L}+|\p{N}{1,3}| ?[^\s\p{L}\p{N}]+[\r\n]*|\s*[\r\n]+|\s+(?!\S)|\s+`),
+			&model.Vocabulary{
+				Values: c.Strings("tokenizer.ggml.tokens"),
+				Scores: c.Floats("tokenizer.ggml.scores"),
+				Types:  c.Uints("tokenizer.ggml.token_type"),
+				BOS:    int32(c.Uint("tokenizer.ggml.bos_token_id")),
+				EOS:    int32(c.Uint("tokenizer.ggml.eos_token_id")),
+			},
+		),
+		Layers: make([]Layer, c.Uint("block_count")),
+		Options: &Options{
+			hiddenSize:        int(c.Uint("embedding_length")),
+			numHeads:          int(c.Uint("attention.head_count")),
+			numKVHeads:        int(c.Uint("attention.head_count_kv")),
+			attnKeyLen:        int(c.Uint("attention.key_length")),
+			attnValLen:        int(c.Uint("attention.value_length")),
+			eps:               c.Float("attention.layer_norm_rms_epsilon"),
+			ropeBase:          c.Float("rope.freq_base", 10000.0),
+			ropeScale:         c.Float("rope.freq_scale", 1.0),
+			attnLogitSoftcap:  c.Float("attn_logit_softcapping"),
+			finalLogitSoftcap: c.Float("final_logit_softcapping"),
+		},
+	}
+
+	slidingWindowLen := int32(c.Uint("attention.sliding_window"))
+	m.Cache = kvcache.NewWrapperCache(kvcache.NewSWACache(slidingWindowLen, m.Shift), kvcache.NewCausalCache(m.Shift))
+
+	return &m, nil
+}
+
+type SelfAttention struct {
+	Query  *nn.Linear `gguf:"attn_q"`
+	Key    *nn.Linear `gguf:"attn_k"`
+	Value  *nn.Linear `gguf:"attn_v"`
+	Output *nn.Linear `gguf:"attn_output"`
+}
+
+func (sa *SelfAttention) Forward(ctx ml.Context, hiddenState, positionIDs ml.Tensor, cache kvcache.Cache, opts *Options) ml.Tensor {
+	batchSize := hiddenState.Dim(1)
+	ropeType := uint32(2)
+
+	q := sa.Query.Forward(ctx, hiddenState)
+	q = q.Reshape(ctx, opts.attnKeyLen, opts.numHeads, batchSize)
+	q = q.RoPE(ctx, positionIDs, nil, uint32(opts.attnKeyLen), ropeType, opts.ropeBase, opts.ropeScale)
+
+	if opts.largeModelScaling {
+		q = q.Scale(ctx, 1.0/math.Sqrt(float64(opts.hiddenSize / opts.numHeads)))
+	} else {
+		q = q.Scale(ctx, 1.0/math.Sqrt(float64(opts.attnKeyLen)))
+	}
+
+	k := sa.Key.Forward(ctx, hiddenState)
+	k = k.Reshape(ctx, opts.attnKeyLen, opts.numKVHeads, batchSize)
+	k = k.RoPE(ctx, positionIDs, nil, uint32(opts.attnKeyLen), ropeType, opts.ropeBase, opts.ropeScale)
+
+	v := sa.Value.Forward(ctx, hiddenState)
+	v = v.Reshape(ctx, opts.attnValLen, opts.numKVHeads, batchSize)
+
+	cache.Put(ctx, k, v)
+	k, v, mask := cache.Get(ctx)
+
+	q = q.Permute(ctx, 0, 2, 1, 3).Contiguous(ctx)
+	k = k.Permute(ctx, 0, 2, 1, 3).Contiguous(ctx)
+	v = v.Permute(ctx, 1, 2, 0, 3).Contiguous(ctx)
+
+	kq := k.Mulmat(ctx, q)
+
+	// logit softcap
+	kq = kq.Scale(ctx, 1.0/float64(opts.attnLogitSoftcap))
+	kq = kq.Tanh(ctx)
+	kq = kq.Scale(ctx, float64(opts.attnLogitSoftcap))
+
+	kq = kq.Add(ctx, mask)
+	kq = kq.Softmax(ctx)
+
+	kqv := v.Mulmat(ctx, kq)
+	kqv = kqv.Permute(ctx, 0, 2, 1, 3).Contiguous(ctx)
+	kqv = kqv.Reshape(ctx, opts.attnValLen*opts.numHeads, batchSize)
+
+	return sa.Output.Forward(ctx, kqv)
+}
+
+func (m *Model) Shift(ctx ml.Context, layer int, key, shift ml.Tensor) (ml.Tensor, error) {
+	return key.RoPE(ctx, shift, nil, uint32(m.Options.attnKeyLen), uint32(2), m.Options.ropeBase, m.Options.ropeScale), nil
+}
+
+type MLP struct {
+	Up   *nn.Linear `gguf:"ffn_up"`
+	Down *nn.Linear `gguf:"ffn_down"`
+	Gate *nn.Linear `gguf:"ffn_gate"`
+}
+
+func (mlp *MLP) Forward(ctx ml.Context, hiddenState ml.Tensor, opts *Options) ml.Tensor {
+	hiddenState = mlp.Gate.Forward(ctx, hiddenState).GELU(ctx).Mul(ctx, mlp.Up.Forward(ctx, hiddenState))
+	return mlp.Down.Forward(ctx, hiddenState)
+}
+
+type Layer struct {
+	AttentionNorm     *nn.RMSNorm `gguf:"attn_norm"`
+	SelfAttention     *SelfAttention
+	PostAttentionNorm *nn.RMSNorm `gguf:"post_attention_norm"`
+	MLPNorm           *nn.RMSNorm `gguf:"ffn_norm"`
+	MLP               *MLP
+	PostMLPNorm       *nn.RMSNorm `gguf:"post_ffw_norm"`
+}
+
+func (l *Layer) Forward(ctx ml.Context, hiddenState, positionIDs ml.Tensor, cache kvcache.Cache, opts *Options) ml.Tensor {
+	residual := hiddenState
+
+	hiddenState = l.AttentionNorm.Forward(ctx, hiddenState, opts.eps)
+	hiddenState = l.SelfAttention.Forward(ctx, hiddenState, positionIDs, cache, opts)
+	hiddenState = l.PostAttentionNorm.Forward(ctx, hiddenState, opts.eps)
+	hiddenState = hiddenState.Add(ctx, residual)
+	residual = hiddenState
+
+	hiddenState = l.MLPNorm.Forward(ctx, hiddenState, opts.eps)
+	hiddenState = l.MLP.Forward(ctx, hiddenState, opts)
+	hiddenState = l.PostMLPNorm.Forward(ctx, hiddenState, opts.eps)
+	return hiddenState.Add(ctx, residual)
+}
+
+func (m *Model) Forward(ctx ml.Context, opts input.Options) (ml.Tensor, error) {
+	inputs, err := ctx.Input().FromIntSlice(opts.Inputs, len(opts.Inputs))
+	if err != nil {
+		return nil, err
+	}
+
+	positions, err := ctx.Input().FromIntSlice(opts.Positions, len(opts.Positions))
+	if err != nil {
+		return nil, err
+	}
+
+	hiddenState := m.TokenEmbedding.Forward(ctx, inputs)
+	hiddenState = hiddenState.Scale(ctx, math.Sqrt(float64(m.Options.hiddenSize)))
+
+	if len(m.Layers) == gemma27BLayerCount {
+		m.Options.largeModelScaling = true
+	}
+
+	for i, layer := range m.Layers {
+		cacheType := i % 2
+		m.Cache.SetLayer(i)
+		wc := m.Cache.(*kvcache.WrapperCache)
+		wc.SetLayerType(cacheType)
+		hiddenState = layer.Forward(ctx, hiddenState, positions, m.Cache, m.Options)
+	}
+
+	hiddenState = m.OutputNorm.Forward(ctx, hiddenState, m.eps)
+	hiddenState = m.Output.Forward(ctx, hiddenState)
+
+	// final logit softcap
+	hiddenState = hiddenState.Scale(ctx, 1.0/float64(m.Options.finalLogitSoftcap))
+	hiddenState = hiddenState.Tanh(ctx)
+	hiddenState = hiddenState.Scale(ctx, float64(m.Options.finalLogitSoftcap))
+
+	outputs, err := ctx.Output().FromIntSlice(opts.Outputs, len(opts.Outputs))
+	if err != nil {
+		return nil, err
+	}
+
+	return hiddenState.Rows(ctx, outputs), nil
+}
+
+func init() {
+	model.Register("gemma2", New)
+}

model/models/gemma3/model.go

@@ -0,0 +1,74 @@
+package gemma3
+
+import (
+	"fmt"
+
+	"github.com/ollama/ollama/kvcache"
+	"github.com/ollama/ollama/ml"
+	"github.com/ollama/ollama/model"
+	"github.com/ollama/ollama/model/input"
+)
+
+type Model struct {
+	model.Base
+	model.SentencePieceModel
+
+	//*VisionModel `gguf:"v,vision"`
+	*TextModel
+
+	//Projector *nn.Linear `gguf:"mm.0"`
+
+	ImageProcessor
+}
+
+func New(c ml.Config) (model.Model, error) {
+	// Verify unified config
+	if c.Uint("vision.block_count") == 0 {
+		return nil, fmt.Errorf("non-unified vision model not supported")
+	}
+	m := Model{
+		SentencePieceModel: model.NewSentencePieceModel(
+			c.String("tokenizer.ggml.pretokenizer", `(?i:'s|'t|'re|'ve|'m|'ll|'d)|[^\r\n\p{L}\p{N}]?\p{L}+|\p{N}{1,3}| ?[^\s\p{L}\p{N}]+[\r\n]*|\s*[\r\n]+|\s+(?!\S)|\s+`),
+			&model.Vocabulary{
+				Values: c.Strings("tokenizer.ggml.tokens"),
+				Scores: c.Floats("tokenizer.ggml.scores"),
+				Types:  c.Uints("tokenizer.ggml.token_type"),
+				BOS:    int32(c.Uint("tokenizer.ggml.bos_token_id")),
+				AddBOS: c.Bool("tokenizer.ggml.add_bos_token", true),
+				EOS:    int32(c.Uint("tokenizer.ggml.eos_token_id")),
+				AddEOS: c.Bool("tokenizer.ggml.add_eos_token", false),
+			},
+		),
+		ImageProcessor: newImageProcessor(c),
+		//VisionModel:    newVisionModel(c),
+		TextModel: newTextModel(c),
+	}
+
+	slidingWindowLen := int32(c.Uint("text.attention.sliding_window"))
+	m.Cache = kvcache.NewWrapperCache(kvcache.NewSWACache(slidingWindowLen, m.Shift), kvcache.NewCausalCache(m.Shift))
+
+	return &m, nil
+}
+
+func (m *Model) Forward(ctx ml.Context, opts input.Options) (ml.Tensor, error) {
+	inputs, err := ctx.Input().FromIntSlice(opts.Inputs, len(opts.Inputs))
+	if err != nil {
+		return nil, err
+	}
+
+	positions, err := ctx.Input().FromIntSlice(opts.Positions, len(opts.Positions))
+	if err != nil {
+		return nil, err
+	}
+
+	outputs, err := ctx.Output().FromIntSlice(opts.Outputs, len(opts.Outputs))
+	if err != nil {
+		return nil, err
+	}
+
+	return m.TextModel.Forward(ctx, inputs, positions, outputs, m.Cache), nil
+}
+
+func init() {
+	model.Register("gemma3", New)
+}

model/models/gemma3/model_text.go

@@ -0,0 +1,193 @@
+package gemma3
+
+import (
+	"math"
+
+	"github.com/ollama/ollama/kvcache"
+	"github.com/ollama/ollama/ml"
+	"github.com/ollama/ollama/ml/nn"
+	"github.com/ollama/ollama/model"
+)
+
+type TextOptions struct {
+	hiddenSize, numHeads, numKVHeads int
+	attnKeyLen, attnValLen           int
+	eps, ropeScale                   float32
+	ropeLocalBase, ropeGlobalBase    float32
+	finalLogitSoftcap                float32
+	largeModelScaling                bool
+}
+
+type TextModel struct {
+	model.Base
+	model.SentencePieceModel
+
+	TokenEmbedding *nn.Embedding `gguf:"token_embd"`
+	Layers         []TextLayer   `gguf:"blk"`
+	OutputNorm     *nn.RMSNorm   `gguf:"output_norm"`
+	Output         *nn.Linear    `gguf:"output,alt:token_embd"`
+
+	*TextOptions
+}
+
+const (
+	gemma27BLayerCount = 46
+)
+
+const (
+	cacheTypeSWA = iota
+	cacheTypeCausal
+)
+
+func newTextModel(c ml.Config) *TextModel {
+	m := TextModel{
+		SentencePieceModel: model.NewSentencePieceModel(
+			c.String("tokenizer.ggml.pretokenizer", `(?i:'s|'t|'re|'ve|'m|'ll|'d)|[^\r\n\p{L}\p{N}]?\p{L}+|\p{N}{1,3}| ?[^\s\p{L}\p{N}]+[\r\n]*|\s*[\r\n]+|\s+(?!\S)|\s+`),
+			&model.Vocabulary{
+				Values: c.Strings("tokenizer.ggml.tokens"),
+				Scores: c.Floats("tokenizer.ggml.scores"),
+				Types:  c.Uints("tokenizer.ggml.token_type"),
+				BOS:    int32(c.Uint("tokenizer.ggml.bos_token_id")),
+				EOS:    int32(c.Uint("tokenizer.ggml.eos_token_id")),
+			},
+		),
+		Layers: make([]TextLayer, c.Uint("block_count")),
+		TextOptions: &TextOptions{
+			hiddenSize:        int(c.Uint("embedding_length")),
+			numHeads:          int(c.Uint("attention.head_count")),
+			numKVHeads:        int(c.Uint("attention.head_count_kv")),
+			attnKeyLen:        int(c.Uint("attention.key_length")),
+			attnValLen:        int(c.Uint("attention.value_length")),
+			eps:               c.Float("text.attention.layer_norm_rms_epsilon"),
+			ropeLocalBase:     c.Float("text.rope.local.freq_base", 10000.0),
+			ropeGlobalBase:    c.Float("text.rope.global.freq_base", 1000000.0),
+			ropeScale:         c.Float("text.rope.freq_scale", 1.0),
+			finalLogitSoftcap: c.Float("text.final_logit_softcapping"),
+		},
+	}
+
+	slidingWindowLen := int32(c.Uint("text.attention.sliding_window"))
+	m.Cache = kvcache.NewWrapperCache(kvcache.NewSWACache(slidingWindowLen, m.Shift), kvcache.NewCausalCache(m.Shift))
+
+	return &m
+}
+
+type TextSelfAttention struct {
+	Query     *nn.Linear  `gguf:"attn_q"`
+	QueryNorm *nn.RMSNorm `gguf:"attn_q_norm"`
+	Key       *nn.Linear  `gguf:"attn_k"`
+	KeyNorm   *nn.RMSNorm `gguf:"attn_k_norm"`
+	Value     *nn.Linear  `gguf:"attn_v"`
+	Output    *nn.Linear  `gguf:"attn_output"`
+}
+
+func (sa *TextSelfAttention) Forward(ctx ml.Context, layer int, hiddenState, positionIDs ml.Tensor, cache kvcache.Cache, opts *TextOptions) ml.Tensor {
+	batchSize := hiddenState.Dim(1)
+	ropeType := uint32(2)
+
+	ropeBase := opts.ropeLocalBase
+	if (layer+1)%6 == 0 {
+		ropeBase = opts.ropeGlobalBase
+	}
+
+	q := sa.Query.Forward(ctx, hiddenState)
+	q = q.Reshape(ctx, opts.attnKeyLen, opts.numHeads, batchSize)
+	q = sa.QueryNorm.Forward(ctx, q, opts.eps)
+	q = q.RoPE(ctx, positionIDs, nil, uint32(opts.attnKeyLen), ropeType, ropeBase, opts.ropeScale)
+
+	if opts.largeModelScaling {
+		q = q.Scale(ctx, 1.0/math.Sqrt(float64(opts.hiddenSize/opts.numHeads)))
+	} else {
+		q = q.Scale(ctx, 1.0/math.Sqrt(float64(opts.attnKeyLen)))
+	}
+
+	k := sa.Key.Forward(ctx, hiddenState)
+	k = k.Reshape(ctx, opts.attnKeyLen, opts.numKVHeads, batchSize)
+	k = sa.KeyNorm.Forward(ctx, k, opts.eps)
+	k = k.RoPE(ctx, positionIDs, nil, uint32(opts.attnKeyLen), ropeType, ropeBase, opts.ropeScale)
+
+	v := sa.Value.Forward(ctx, hiddenState)
+	v = v.Reshape(ctx, opts.attnValLen, opts.numKVHeads, batchSize)
+
+	scaleFactor := 1.0
+	kqv := nn.Attention(ctx, q, k, v, scaleFactor, cache)
+	kqv = kqv.Reshape(ctx, opts.attnValLen*opts.numHeads, batchSize)
+
+	return sa.Output.Forward(ctx, kqv)
+}
+
+func (m *TextModel) Shift(ctx ml.Context, layer int, key, shift ml.Tensor) (ml.Tensor, error) {
+	ropeBase := m.TextOptions.ropeLocalBase
+	if (layer+1)%6 == 0 {
+		ropeBase = m.TextOptions.ropeGlobalBase
+	}
+
+	return key.RoPE(ctx, shift, nil, uint32(m.TextOptions.attnKeyLen), uint32(2), ropeBase, m.TextOptions.ropeScale), nil
+}
+
+type TextMLP struct {
+	Up   *nn.Linear `gguf:"ffn_up"`
+	Down *nn.Linear `gguf:"ffn_down"`
+	Gate *nn.Linear `gguf:"ffn_gate"`
+}
+
+func (mlp *TextMLP) Forward(ctx ml.Context, hiddenState ml.Tensor, opts *TextOptions) ml.Tensor {
+	hiddenState = mlp.Gate.Forward(ctx, hiddenState).GELU(ctx).Mul(ctx, mlp.Up.Forward(ctx, hiddenState))
+	return mlp.Down.Forward(ctx, hiddenState)
+}
+
+type TextLayer struct {
+	AttentionNorm     *nn.RMSNorm `gguf:"attn_norm"`
+	SelfAttention     *TextSelfAttention
+	PostAttentionNorm *nn.RMSNorm `gguf:"post_attention_norm"`
+	MLPNorm           *nn.RMSNorm `gguf:"ffn_norm"`
+	MLP               *TextMLP
+	PostMLPNorm       *nn.RMSNorm `gguf:"post_ffw_norm"`
+}
+
+func (l *TextLayer) Forward(ctx ml.Context, layer int, hiddenState, positionIDs ml.Tensor, cache kvcache.Cache, opts *TextOptions) ml.Tensor {
+	residual := hiddenState
+
+	hiddenState = l.AttentionNorm.Forward(ctx, hiddenState, opts.eps)
+	hiddenState = l.SelfAttention.Forward(ctx, layer, hiddenState, positionIDs, cache, opts)
+	hiddenState = l.PostAttentionNorm.Forward(ctx, hiddenState, opts.eps)
+	hiddenState = hiddenState.Add(ctx, residual)
+	residual = hiddenState
+
+	hiddenState = l.MLPNorm.Forward(ctx, hiddenState, opts.eps)
+	hiddenState = l.MLP.Forward(ctx, hiddenState, opts)
+	hiddenState = l.PostMLPNorm.Forward(ctx, hiddenState, opts.eps)
+	return hiddenState.Add(ctx, residual)
+}
+
+func (m *TextModel) Forward(ctx ml.Context, inputs, positions, outputs ml.Tensor, cache kvcache.Cache) ml.Tensor {
+	hiddenState := m.TokenEmbedding.Forward(ctx, inputs)
+	hiddenState = hiddenState.Scale(ctx, math.Sqrt(float64(m.TextOptions.hiddenSize)))
+
+	if len(m.Layers) == gemma27BLayerCount {
+		m.TextOptions.largeModelScaling = true
+	}
+
+	for i, layer := range m.Layers {
+		// gemma alternates between the sliding window (local) and causal (global)
+		// kv cache every 6 layers
+		cacheType := cacheTypeSWA
+		if (i+1)%6 == 0 {
+			cacheType = cacheTypeCausal
+		}
+		cache.SetLayer(i)
+		wc := cache.(*kvcache.WrapperCache)
+		wc.SetLayerType(cacheType)
+		hiddenState = layer.Forward(ctx, i, hiddenState, positions, cache, m.TextOptions)
+	}
+
+	hiddenState = m.OutputNorm.Forward(ctx, hiddenState, m.eps)
+	hiddenState = m.Output.Forward(ctx, hiddenState)
+
+	// final logit softcap
+	hiddenState = hiddenState.Scale(ctx, 1.0/float64(m.TextOptions.finalLogitSoftcap))
+	hiddenState = hiddenState.Tanh(ctx)
+	hiddenState = hiddenState.Scale(ctx, float64(m.TextOptions.finalLogitSoftcap))
+
+	return hiddenState.Rows(ctx, outputs)
+}

model/models/gemma3/process_image.go

@@ -0,0 +1,57 @@
+package gemma3
+
+import (
+	"image"
+
+	"github.com/ollama/ollama/ml"
+	"github.com/ollama/ollama/model/imageproc"
+)
+
+type ImageProcessor struct {
+	imageSize, numChannels int
+}
+
+func newImageProcessor(c ml.Config) ImageProcessor {
+	return ImageProcessor{
+		imageSize:   int(c.Uint("vision.image_size")),
+		numChannels: int(c.Uint("vision.num_channels")),
+	}
+}
+
+func (p *ImageProcessor) pack(img image.Image, mean, std [3]float32) []float32 {
+	var pixelVals []float32
+
+	bounds := img.Bounds()
+	var rVals, gVals, bVals []float32
+	for y := bounds.Min.Y; y < bounds.Max.Y; y++ {
+		for x := bounds.Min.X; x < bounds.Max.X; x++ {
+			c := img.At(x, y)
+			r, g, b, _ := c.RGBA()
+			rVal := float32(r>>8) / 255.0
+			gVal := float32(g>>8) / 255.0
+			bVal := float32(b>>8) / 255.0
+
+			rVal = (rVal - mean[0]) / std[0]
+			gVal = (gVal - mean[1]) / std[1]
+			bVal = (bVal - mean[2]) / std[2]
+
+			rVals = append(rVals, rVal)
+			gVals = append(gVals, gVal)
+			bVals = append(bVals, bVal)
+		}
+	}
+	pixelVals = append(pixelVals, rVals...)
+	pixelVals = append(pixelVals, gVals...)
+	pixelVals = append(pixelVals, bVals...)
+
+	return pixelVals
+}
+
+func (p ImageProcessor) ProcessImage(img image.Image) ([]float32, error) {
+	outputSize := image.Point{p.imageSize, p.imageSize}
+	newImage := imageproc.Composite(img)
+	newImage = imageproc.Resize(newImage, outputSize, imageproc.ResizeBilinear)
+
+	data := p.pack(newImage, imageproc.ImageNetStandardMean, imageproc.ImageNetStandardSTD)
+	return data, nil
+}

model/models/llama/model.go

@@ -76,14 +76,15 @@ type SelfAttention struct {
 func (sa *SelfAttention) Forward(ctx ml.Context, hiddenState, positionIDs ml.Tensor, cache kvcache.Cache, opts *Options) ml.Tensor {
 	batchSize := hiddenState.Dim(1)
 	headDim := opts.hiddenSize / opts.numHeads
+	ropeType := uint32(0)
 
 	q := sa.Query.Forward(ctx, hiddenState)
 	q = q.Reshape(ctx, headDim, opts.numHeads, batchSize)
-	q = q.RoPE(ctx, positionIDs, sa.RopeFactors, opts.ropeDim, opts.ropeBase, opts.ropeScale)
+	q = q.RoPE(ctx, positionIDs, sa.RopeFactors, opts.ropeDim, ropeType, opts.ropeBase, opts.ropeScale)
 
 	k := sa.Key.Forward(ctx, hiddenState)
 	k = k.Reshape(ctx, headDim, opts.numKVHeads, batchSize)
-	k = k.RoPE(ctx, positionIDs, sa.RopeFactors, opts.ropeDim, opts.ropeBase, opts.ropeScale)
+	k = k.RoPE(ctx, positionIDs, sa.RopeFactors, opts.ropeDim, ropeType, opts.ropeBase, opts.ropeScale)
 
 	v := sa.Value.Forward(ctx, hiddenState)
 	v = v.Reshape(ctx, headDim, opts.numKVHeads, batchSize)
@@ -96,7 +97,7 @@ func (sa *SelfAttention) Forward(ctx ml.Context, hiddenState, positionIDs ml.Ten
 }
 
 func (m *Model) Shift(ctx ml.Context, layer int, key, shift ml.Tensor) (ml.Tensor, error) {
-	return key.RoPE(ctx, shift, m.Layers[layer].SelfAttention.RopeFactors, m.ropeDim, m.ropeBase, m.ropeScale), nil
+	return key.RoPE(ctx, shift, m.Layers[layer].SelfAttention.RopeFactors, uint32(0), m.ropeDim, m.ropeBase, m.ropeScale), nil
 }
 
 type MLP struct {

model/models/mllama/model_text.go

@@ -20,14 +20,15 @@ type TextSelfAttention struct {
 func (sa *TextSelfAttention) Forward(ctx ml.Context, hiddenState, positions, _ ml.Tensor, cache *kvcache.WrapperCache, opts *TextModelOptions) ml.Tensor {
 	batchSize := hiddenState.Dim(1)
 	headDim := opts.hiddenSize / opts.numHeads
+	ropeType := uint32(0)
 
 	query := sa.Query.Forward(ctx, hiddenState)
 	query = query.Reshape(ctx, headDim, opts.numHeads, batchSize)
-	query = query.RoPE(ctx, positions, sa.RopeFactors, opts.ropeDim, opts.ropeBase, opts.ropeScale)
+	query = query.RoPE(ctx, positions, sa.RopeFactors, opts.ropeDim, ropeType, opts.ropeBase, opts.ropeScale)
 
 	key := sa.Key.Forward(ctx, hiddenState)
 	key = key.Reshape(ctx, headDim, opts.numKVHeads, batchSize)
-	key = key.RoPE(ctx, positions, sa.RopeFactors, opts.ropeDim, opts.ropeBase, opts.ropeScale)
+	key = key.RoPE(ctx, positions, sa.RopeFactors, opts.ropeDim,  ropeType, opts.ropeBase, opts.ropeScale)
 
 	value := sa.Value.Forward(ctx, hiddenState)
 	value = value.Reshape(ctx, headDim, opts.numKVHeads, batchSize)
@@ -40,8 +41,9 @@ func (sa *TextSelfAttention) Forward(ctx ml.Context, hiddenState, positions, _ m
 }
 
 func (m *TextModel) Shift(ctx ml.Context, layer int, key, shift ml.Tensor) (ml.Tensor, error) {
+	// This will only get called for layers in the cache, which are just the self attention layers
 	if sa, ok := m.Transformer.Layers[layer].(*TextSelfAttentionDecoderLayer); ok {
-		return key.RoPE(ctx, shift, sa.SelfAttention.RopeFactors, m.ropeDim, m.ropeBase, m.ropeScale), nil
+		return key.RoPE(ctx, shift, sa.SelfAttention.RopeFactors, m.ropeDim, uint32(0), m.ropeBase, m.ropeScale), nil
 	}
 
 	return key, nil

model/models/models.go

@@ -1,6 +1,8 @@
 package models
 
 import (
+	_ "github.com/ollama/ollama/model/models/gemma2"
+	_ "github.com/ollama/ollama/model/models/gemma3"
 	_ "github.com/ollama/ollama/model/models/llama"
 	_ "github.com/ollama/ollama/model/models/mllama"
 )